Course Setup

install.packages("tidyverse")

library(tidyverse)

[30m-- [1mAttaching packages[22m --------------------------------------- tidyverse 1.2.1 --[39m
[30m[32mv[30m [34mggplot2[30m 3.0.0     [32mv[30m [34mpurrr  [30m 0.2.5
[32mv[30m [34mtibble [30m 1.4.2     [32mv[30m [34mdplyr  [30m 0.7.6
[32mv[30m [34mtidyr  [30m 0.8.1     [32mv[30m [34mstringr[30m 1.3.1
[32mv[30m [34mreadr  [30m 1.1.1     [32mv[30m [34mforcats[30m 0.3.0[39m
package 㤼㸱ggplot2㤼㸲 was built under R version 3.5.1package 㤼㸱dplyr㤼㸲 was built under R version 3.5.1[30m-- [1mConflicts[22m ------------------------------------------ tidyverse_conflicts() --
[31mx[30m [34mdplyr[30m::[32mfilter()[30m masks [34mstats[30m::filter()
[31mx[30m [34mdplyr[30m::[32mlag()[30m    masks [34mstats[30m::lag()[39m

Explore Data

First ggplot

ggplot(data = midwest) +
  geom_point(mapping = aes(x = popdensity, y = percollege))

Equivalent Code

ggplot(midwest) +
  geom_point(aes(x = popdensity, y = percollege))

Your Turn

Try plotting popdensity by state.
Try plotting county by state.
- Does this plot work?
Bonus: Try just using the ggplot(data = midwest) from above.
- What do you get?
- Does this make sense?

Add Aesthetics

ggplot(midwest) +
  geom_point(aes(x = popdensity, y = percollege, color = state))

Global Aesthetics

ggplot(midwest) +
  geom_point(aes(x = popdensity, y = percollege), color = 'pink')

Your Turn

Instead of using colors, make the shape of the points different for each state.
Instead of color, use alpha instead.
- What does this do to the plot?
Try the following command: colors().
- Try a few colors to find your favorite.
What happens if you use the following code:

ggplot(midwest) + 
  geom_point(aes(x = popdensity, y = percollege, color = 'green'))

Additional Geoms

ggplot(midwest) +
  geom_smooth(aes(x = popdensity, y = percollege))

Add more Aesthetics

ggplot(midwest) +
  geom_smooth(aes(x = popdensity, y = percollege, linetype = state), 
              se = FALSE)

Your Turn

It is possible to combine geoms, which we will do next, but try it first. Try to recreate this plot.

Layered ggplot

ggplot(midwest) +
  geom_point(aes(x = popdensity, y = percollege, color = state)) +
  geom_smooth(aes(x = popdensity, y = percollege, color = state), 
              se = FALSE)

Remove duplicate aesthetics

ggplot(midwest, 
       aes(x = popdensity, y = percollege, color = state)) +
  geom_point() +
  geom_smooth(se = FALSE)

Your Turn

Can you recreate the following figure?

Brief plot customization

ggplot(midwest, 
       aes(x = popdensity, y = percollege, color = state)) +
  geom_point() + 
  scale_x_continuous("Population Density", 
                     breaks = seq(0, 80000, 20000)) + 
  scale_y_continuous("Percent College Graduates") + 
  scale_color_discrete("State")

Brief plot customization Output

Additional ggplot2 resources

ggplot2 website: http://docs.ggplot2.org/current/index.html
ggplot2 book: http://www.springer.com/us/book/9780387981413
R graphics cookbook: http://www.cookbook-r.com/Graphs/

R works as a calculator

1 + 2 - 3

[1] 0

5 * 7

[1] 35

2/1

[1] 2

R Calculator 2

sqrt(4)

[1] 2

2^2

[1] 4

Can save objects to use later

x <- 1 + 3
x

[1] 4

x * 3

[1] 12

R is case sensitive

case_sensitive <- 10
Case_sensitive

Error: object 'Case_sensitive' not found

R Functions

set.seed(1)
rnorm(n = 5, mean = 0, sd = 1)

[1] -0.6264538  0.1836433 -0.8356286  1.5952808  0.3295078

set.seed(1)
rnorm(5, 0, 1)

[1] -0.6264538  0.1836433 -0.8356286  1.5952808  0.3295078

set.seed(1)
rnorm(sd = 1, n = 5, mean = 0)

[1] -0.6264538  0.1836433 -0.8356286  1.5952808  0.3295078

Working through Errors

Use ?function_name to explore the details of the function. The examples at the bottom of every R help page can be especially helpful.
- https://www.rdocumentation.org/ provided by DataCamp is a great alternative as well.
If this does not help, copy and paste the error and search on the internet.

Using `dplyr` for data manipulation

The dplyr package uses verbs for common data manipulation tasks. These include:

filter()
count()
arrange()
select()
mutate()
summarise()

Data

https://fivethirtyeight.com/features/both-republicans-and-democrats-have-an-age-problem/

install.packages('fivethirtyeight')
library(fivethirtyeight)

Using `filter`

filter(congress_age, congress == 80)

Save filtered results to object

congress_80 <- filter(congress_age, congress == 80)

Other operators for numbers

>
<
>=
<=

Your Turn

Select all rows where the congress member was older than 80 at the start of the term.
Use the is.na function to identify congress members that have missing middlenames.

Filter character variables

filter(congress_age, chamber == 'senate')

Combine Operations - AND

filter(congress_age, congress == 80, chamber == 'senate')

Equivalent AND Statement

filter(congress_age, congress == 80 & chamber == 'senate')

Filter - OR

filter(congress_age, congress == 80 | congress == 81)

`%in%`

filter(congress_age, congress %in% c(80, 81))

Not Operator

filter(congress_age, congress != 80)

Not Operator 2

filter(congress_age, congress == 80 & !chamber == 'senate')

Your Turn

Select the Senators from Iowa.
Select the Senators from Iowa that are not inbumbents.

All boolean options

Using `count`

count(congress_age, party, incumbent)

Using `arrange`

arrange(congress_age, state, party)

Descending Order

arrange(congress_age, desc(congress))

Your Turn

Count the number of congress members from each party that are older than 80 at term start.
Arrange the result from above by the variable n.

Using `select`

select(congress_age, congress, chamber, party, age)

Helper functions

starts_with()
ends_with()
contains()
matches()
num_range()
:
everything()

`starts_with` helper

select(congress_age, starts_with('s'))

Contains helper

select(congress_age, contains('name'))

Colon

select(congress_age, congress:birthday)

Drop variables

select(congress_age, -firstname, -state, -party, -incumbent, -chamber)

Reorder with `everything`

select(congress_age, congress, chamber, incumbent, age, everything())

`rename` function

rename(congress_age, first_name = firstname, last_name = lastname)

Your Turn

Using the dplyr helper functions, select all the variables that start with the letter ‘c’.
Rename the first three variables in the congress data to ‘x1’, ‘x2’, ‘x3’.
After renaming the first three variables, use this new data (ensure you saved the previous step to an object) to select these three variables with the num_range function.

Using `mutate`

congress_red <- select(congress_age, congress, chamber, state, party)
mutate(congress_red, 
       democrat = ifelse(party == 'D', 1, 0),
       num_democrat = sum(democrat)
       )

Your Turn

Using the diamonds data, use ?diamonds for more information on the data, use the mutate function to calculate the price per carat. Hint, this operation would involve standardizing the price variable so that all are comparable at 1 carat.
Using mutate, calculate the rank of the original price variable and the new price variable calculated above using the min_rank function. Are there differences in the ranking of the prices?

Using `summarise`

congress_2 <- mutate(congress_age, 
       democrat = ifelse(party == 'D', 1, 0)
       )
summarise(congress_2, 
          num_democrat = sum(democrat)
          )

`group_by`

congress_grp <- group_by(congress_2, congress)
summarise(congress_grp, 
          num_democrat = sum(democrat),
          total = n(),
          prop_democrat = num_democrat / total
)

Explore trend

num_dem <- summarise(congress_grp, 
                     num_democrat = sum(democrat),
                     total = n(),
                     prop_democrat = num_democrat / total
)
ggplot(num_dem, aes(x = congress, y = prop_democrat)) + 
  geom_line()

Explore trend output

Your Turn

Suppose we wanted to calculate the number and proportion of republicans instead of democrats, assuming these are the only two parties, edit the summarise command above to calculate these values.
Suppose instead of using sum(democrat) above, we used mean(democrat), what does this value return? Why does it return this value?

`group_by` with `mutate`

congress_red <- select(congress_age, congress, chamber, state, party)
congress_grp <- group_by(congress_red, congress)

mutate(congress_grp, 
       democrat = ifelse(party == 'D', 1, 0),
       num_democrat = sum(democrat),
       total = n(),
       prop_democrat = num_democrat / total
)

`group_by` with `mutate` output

Chaining operations

summarise(
  group_by(
    mutate(
      filter(
        congress_age, congress >= 100
      ), 
      democrat = ifelse(party == 'D', 1, 0)
    ),
    congress, chamber
  ),
  num_democrat = sum(democrat),
  total = n(),
  prop_democrat = num_democrat / total
)

The pipe `%>%` is the answer

congress_age %>%
  filter(congress >= 100) %>%
  mutate(democrat = ifelse(party == 'D', 1, 0)) %>%
  group_by(congress, chamber) %>%
  summarise(
    num_democrat = sum(democrat),
    total = n(),
    prop_democrat = num_democrat / total
  )

The two are identical

pipe_congress <- congress_age %>%
  filter(congress >= 100) %>%
  mutate(democrat = ifelse(party == 'D', 1, 0)) %>%
  group_by(congress, chamber) %>%
  summarise(
    num_democrat = sum(democrat),
    total = n(),
    prop_democrat = num_democrat / total
  )
nested_congress <- summarise(
  group_by(
    mutate(
      filter(
        congress_age, congress >= 100
      ), 
      democrat = ifelse(party == 'D', 1, 0)
    ),
    congress, chamber
  ),
  num_democrat = sum(democrat),
  total = n(),
  prop_democrat = num_democrat / total
)
identical(pipe_congress, nested_congress)

[1] TRUE

Your Turn

Look at the following nested code and determine what is being done. Then translate this code to use the pipe operator.

summarise(
  group_by(
    mutate(
      filter(
        diamonds, 
        color %in% c('D', 'E', 'F') & cut %in% c('Fair', 'Good', 'Very Good')
      ),
      f_color = ifelse(color == 'F', 1, 0),
      vg_cut = ifelse(cut == 'Very Good', 1, 0)
    ),
    clarity
  ),
  avg = mean(carat),
  sd = sd(carat),
  avg_p = mean(price),
  num = n(),
  summary_f_color = mean(f_color),
  summary_vg_cut = mean(vg_cut)
)

Read in your own data

We will use data posted to GitHub: https://github.com/lebebr01/iowa_data_science/tree/master/data

Data Import

ufo <- read_csv("https://raw.githubusercontent.com/lebebr01/iowa_data_science/master/data/ufo.csv")

Parsed with column specification:
cols(
  `Date / Time` = col_character(),
  City = col_character(),
  State = col_character(),
  Shape = col_character(),
  Duration = col_character(),
  Summary = col_character(),
  Posted = col_character()
)

Show Data

ufo

Other text formats

tsv - tab separated files - read_tsv
fixed width files - read_fwf
white space generally - read_table
delimiter generally - read_delim

Your Turn

There is a tsv file posted on icon called “lotr_clean.tsv”. Download this and read this data file into R.
Instead of specifying the path, use the function file.choose(). For example, read_tsv(file.choose()).
- What does this function do?
- Would you recommend this to be used in a reproducible document?

Excel Files

install.packages('readxl')

`read_excel`

library(readxl)
read_excel('data/titanic.xlsx')

Additional Resources

R for Data Science: http://r4ds.had.co.nz/

---
title: "Introduction to tidyverse/R"
author: "Brandon LeBeau"
date: "January 10, 2018"
output: html_notebook
---

# Course Setup
```{r setup_chunks, echo = FALSE}
knitr::opts_chunk$set(fig.width=12, fig.height=6, fig.cap = NULL) 
```

```{r setup, message = FALSE, eval = FALSE}
install.packages("tidyverse")
```

```{r library}
library(tidyverse)
```

# Explore Data
```{r data, echo = FALSE, results = 'asis'}
midwest
```

# First ggplot
```{r plot1}
ggplot(data = midwest) +
  geom_point(mapping = aes(x = popdensity, y = percollege))
```

# Equivalent Code
```{r plot1_reduced}
ggplot(midwest) +
  geom_point(aes(x = popdensity, y = percollege))
```

# Your Turn
1. Try plotting `popdensity` by `state`.
2. Try plotting `county` by `state`. 
    + Does this plot work?
3. Bonus: Try just using the `ggplot(data = midwest)` from above. 
    + What do you get? 
    + Does this make sense?

# Add Aesthetics
```{r aesthetic}
ggplot(midwest) +
  geom_point(aes(x = popdensity, y = percollege, color = state))
```

# Global Aesthetics
```{r global_aes}
ggplot(midwest) +
  geom_point(aes(x = popdensity, y = percollege), color = 'pink')
```

# Your Turn
1. Instead of using colors, make the shape of the points different for each state.
2. Instead of color, use `alpha` instead. 
    + What does this do to the plot?
3. Try the following command: `colors()`. 
    + Try a few colors to find your favorite.
4. What happens if you use the following code:
```{r glob_aes, fig.show = 'hide'}
ggplot(midwest) + 
  geom_point(aes(x = popdensity, y = percollege, color = 'green'))
```

# Additional Geoms
```{r smooth, message = FALSE}
ggplot(midwest) +
  geom_smooth(aes(x = popdensity, y = percollege))
```

# Add more Aesthetics
```{r smooth_states, message = FALSE}
ggplot(midwest) +
  geom_smooth(aes(x = popdensity, y = percollege, linetype = state), 
              se = FALSE)
```

# Your Turn
1. It is possible to combine geoms, which we will do next, but try it first. Try to recreate this plot.
```{r combine, echo = FALSE, message = FALSE}
ggplot(midwest) +
  geom_point(aes(x = popdensity, y = percollege, color = state)) +
  geom_smooth(aes(x = popdensity, y = percollege, color = state), 
              se = FALSE)
```

# Layered ggplot
```{r combine_geoms, message = FALSE}
ggplot(midwest) +
  geom_point(aes(x = popdensity, y = percollege, color = state)) +
  geom_smooth(aes(x = popdensity, y = percollege, color = state), 
              se = FALSE)
```

# Remove duplicate aesthetics
```{r two_geoms, message = FALSE}
ggplot(midwest, 
       aes(x = popdensity, y = percollege, color = state)) +
  geom_point() +
  geom_smooth(se = FALSE)
```

# Your Turn
1. Can you recreate the following figure?
```{r differ_aes, message = FALSE, echo = FALSE}
ggplot(midwest, aes(x = popdensity, y = percollege)) +
  geom_point(aes(color = state)) +
  geom_smooth(se = FALSE)
```

# Brief plot customization
```{r breaks_x, eval = FALSE}
ggplot(midwest, 
       aes(x = popdensity, y = percollege, color = state)) +
  geom_point() + 
  scale_x_continuous("Population Density", 
                     breaks = seq(0, 80000, 20000)) + 
  scale_y_continuous("Percent College Graduates") + 
  scale_color_discrete("State")
```

# Brief plot customization Output
```{r breaks_x2, echo = FALSE}
ggplot(midwest, 
       aes(x = popdensity, y = percollege, color = state)) +
  geom_point() + 
  scale_x_continuous("Population Density", 
                     breaks = seq(0, 80000, 20000)) + 
  scale_y_continuous("Percent College Graduates") + 
  scale_color_discrete("State")
```


# Additional ggplot2 resources
+ ggplot2 website: <http://docs.ggplot2.org/current/index.html>
+ ggplot2 book: <http://www.springer.com/us/book/9780387981413>
+ R graphics cookbook: <http://www.cookbook-r.com/Graphs/>


# R works as a calculator
```{r calc}
1 + 2 - 3
5 * 7
2/1
```

# R Calculator 2
```{r calc2}
sqrt(4)
2^2
```



# Can save objects to use later
```{r object}
x <- 1 + 3
x
```

```{r object2}
x * 3
```

# R is case sensitive
```{r case, error = TRUE}
case_sensitive <- 10
Case_sensitive
```

# R Functions
```{r rnorm}
set.seed(1)
rnorm(n = 5, mean = 0, sd = 1)
```
```{r rnorm2}
set.seed(1)
rnorm(5, 0, 1)
```
```{r rnorm3}
set.seed(1)
rnorm(sd = 1, n = 5, mean = 0)
```

# Working through Errors
1. Use `?function_name` to explore the details of the function. The examples at the bottom of every R help page can be especially helpful.
    + <https://www.rdocumentation.org/> provided by DataCamp is a great alternative as well.
2. If this does not help, copy and paste the error and search on the internet.

# Using `dplyr` for data manipulation
The `dplyr` package uses verbs for common data manipulation tasks. These include:

- `filter()`
- `count()`
- `arrange()`
- `select()`
- `mutate()`
- `summarise()`

# Data
<https://fivethirtyeight.com/features/both-republicans-and-democrats-have-an-age-problem/>

```{r fivethirtyeight, eval = FALSE}
install.packages('fivethirtyeight')
library(fivethirtyeight)
```

```{r congress, echo = FALSE}
library(fivethirtyeight)
congress_age[1:100, ]
```


# Using `filter`
```{r 80th}
filter(congress_age, congress == 80)
```

# Save filtered results to object
```{r 80th_save}
congress_80 <- filter(congress_age, congress == 80)
```

# Other operators for numbers
- `>`
- `<`
- `>=`
- `<=`

# Your Turn
1. Select all rows where the congress member was older than 80 at the start of the term.
2. Use the `is.na` function to identify congress members that have missing middlenames.

# Filter character variables
```{r senate}
filter(congress_age, chamber == 'senate')
```

# Combine Operations - AND
```{r 80senate}
filter(congress_age, congress == 80, chamber == 'senate')
```

# Equivalent AND Statement
```{r 80senate2}
filter(congress_age, congress == 80 & chamber == 'senate')
```

# Filter - OR
```{r filter_or}
filter(congress_age, congress == 80 | congress == 81)
```

# `%in%`
```{r filter_2}
filter(congress_age, congress %in% c(80, 81))
```

# Not Operator
```{r not_80}
filter(congress_age, congress != 80)
```

# Not Operator 2
```{r 80_notsenate}
filter(congress_age, congress == 80 & !chamber == 'senate')
```


# Your Turn
1. Select the Senators from Iowa.
2. Select the Senators from Iowa that are not inbumbents.

# All boolean options
![](figures/boolean.png)

# Using `count`
```{r count}
count(congress_age, party, incumbent)
```

# Using `arrange`
```{r simple_arrange}
arrange(congress_age, state, party)
```

# Descending Order
```{r descend}
arrange(congress_age, desc(congress))
```

# Your Turn
1. Count the number of congress members from each party that are older than 80 at term start.
2. Arrange the result from above by the variable n.

# Using `select`
```{r select}
select(congress_age, congress, chamber, party, age)
```

# Helper functions
- `starts_with()`
- `ends_with()`
- `contains()`
- `matches()`
- `num_range()`
- `:`
- `everything()`

# `starts_with` helper
```{r starts_with}
select(congress_age, starts_with('s'))
```


# Contains helper
```{r contains}
select(congress_age, contains('name'))
```

# Colon
```{r colon}
select(congress_age, congress:birthday)
```

# Drop variables
```{r drop}
select(congress_age, -firstname, -state, -party, -incumbent, -chamber)
```

# Reorder with `everything`
```{r reorder}
select(congress_age, congress, chamber, incumbent, age, everything())
```

# `rename` function
```{r rename}
rename(congress_age, first_name = firstname, last_name = lastname)
```

# Your Turn
1. Using the `dplyr` helper functions, select all the variables that start with the letter 'c'.
2. Rename the first three variables in the congress data to 'x1', 'x2', 'x3'.
3. After renaming the first three variables, use this new data (ensure you saved the previous step to an object) to select these three variables with the `num_range` function.

# Using `mutate`
```{r mutate, error = FALSE}
congress_red <- select(congress_age, congress, chamber, state, party)

mutate(congress_red, 
       democrat = ifelse(party == 'D', 1, 0),
       num_democrat = sum(democrat)
       )
```

# Your Turn
1. Using the `diamonds` data, use `?diamonds` for more information on the data, use the `mutate` function to calculate the price per carat. Hint, this operation would involve standardizing the price variable so that all are comparable at 1 carat.
2. Using `mutate`, calculate the rank of the original price variable and the new price variable calculated above using the `min_rank` function. Are there differences in the ranking of the prices?

# Using `summarise`
```{r summarise}
congress_2 <- mutate(congress_age, 
       democrat = ifelse(party == 'D', 1, 0)
       )

summarise(congress_2, 
          num_democrat = sum(democrat)
          )
```

# `group_by`
```{r group_by}
congress_grp <- group_by(congress_2, congress)

summarise(congress_grp, 
          num_democrat = sum(democrat),
          total = n(),
          prop_democrat = num_democrat / total
)
```

# Explore trend
```{r trend, eval = FALSE}
num_dem <- summarise(congress_grp, 
                     num_democrat = sum(democrat),
                     total = n(),
                     prop_democrat = num_democrat / total
)
ggplot(num_dem, aes(x = congress, y = prop_democrat)) + 
  geom_line()
```

# Explore trend output
```{r trend2, echo = FALSE}
num_dem <- summarise(congress_grp, 
                     num_democrat = sum(democrat),
                     total = n(),
                     prop_democrat = num_democrat / total
)
ggplot(num_dem, aes(x = congress, y = prop_democrat)) + 
  geom_line()
```

# Your Turn
1. Suppose we wanted to calculate the number and proportion of republicans instead of democrats, assuming these are the only two parties, edit the `summarise` command above to calculate these values. 
2. Suppose instead of using `sum(democrat)` above, we used `mean(democrat)`, what does this value return? Why does it return this value?


# `group_by` with `mutate`
```{r mutate_group, eval = FALSE}
congress_red <- select(congress_age, congress, chamber, state, party)
congress_grp <- group_by(congress_red, congress)

mutate(congress_grp, 
       democrat = ifelse(party == 'D', 1, 0),
       num_democrat = sum(democrat),
       total = n(),
       prop_democrat = num_democrat / total
)
```

# `group_by` with `mutate` output
```{r mutate_group2, echo = FALSE}
congress_red <- select(congress_age, congress, chamber, state, party)
congress_grp <- group_by(congress_red, congress)

mutate(congress_grp, 
       democrat = ifelse(party == 'D', 1, 0),
       num_democrat = sum(democrat),
       total = n(),
       prop_democrat = num_democrat / total
)
```

# Chaining operations
```{r chain_difficult, eval = FALSE}
summarise(
  group_by(
    mutate(
      filter(
        congress_age, congress >= 100
      ), 
      democrat = ifelse(party == 'D', 1, 0)
    ),
    congress, chamber
  ),
  num_democrat = sum(democrat),
  total = n(),
  prop_democrat = num_democrat / total
)
```

# The pipe `%>%` is the answer
```{r pipe, eval = FALSE}
congress_age %>%
  filter(congress >= 100) %>%
  mutate(democrat = ifelse(party == 'D', 1, 0)) %>%
  group_by(congress, chamber) %>%
  summarise(
    num_democrat = sum(democrat),
    total = n(),
    prop_democrat = num_democrat / total
  )
```

# The two are identical
```{r identical}
pipe_congress <- congress_age %>%
  filter(congress >= 100) %>%
  mutate(democrat = ifelse(party == 'D', 1, 0)) %>%
  group_by(congress, chamber) %>%
  summarise(
    num_democrat = sum(democrat),
    total = n(),
    prop_democrat = num_democrat / total
  )

nested_congress <- summarise(
  group_by(
    mutate(
      filter(
        congress_age, congress >= 100
      ), 
      democrat = ifelse(party == 'D', 1, 0)
    ),
    congress, chamber
  ),
  num_democrat = sum(democrat),
  total = n(),
  prop_democrat = num_democrat / total
)

identical(pipe_congress, nested_congress)
```


# Your Turn
1. Look at the following nested code and determine what is being done. Then translate this code to use the pipe operator.
```{r code_pipe_examp, eval = FALSE}
summarise(
  group_by(
    mutate(
      filter(
        diamonds, 
        color %in% c('D', 'E', 'F') & cut %in% c('Fair', 'Good', 'Very Good')
      ),
      f_color = ifelse(color == 'F', 1, 0),
      vg_cut = ifelse(cut == 'Very Good', 1, 0)
    ),
    clarity
  ),
  avg = mean(carat),
  sd = sd(carat),
  avg_p = mean(price),
  num = n(),
  summary_f_color = mean(f_color),
  summary_vg_cut = mean(vg_cut)
)
```

# Read in your own data
- We will use data posted to GitHub: <https://github.com/lebebr01/iowa_data_science/tree/master/data>

# Data Import
```{r ufo_read}
ufo <- read_csv("https://raw.githubusercontent.com/lebebr01/iowa_data_science/master/data/ufo.csv")
```

# Show Data
```{r ufo_data}
ufo
```


# Other text formats
- tsv - tab separated files - `read_tsv`
- fixed width files - `read_fwf`
- white space generally - `read_table`
- delimiter generally - `read_delim`

# Your Turn
1. There is a tsv file posted on icon called "lotr_clean.tsv". Download this and read this data file into R. 
2. Instead of specifying the path, use the function `file.choose()`. For example, `read_tsv(file.choose())`. 
    + What does this function do? 
    + Would you recommend this to be used in a reproducible document?
    
# Excel Files
```{r readxl, eval = FALSE}
install.packages('readxl')
```

# `read_excel`
```{r read_excel}
library(readxl)
read_excel('data/titanic.xlsx')
```

# Additional Resources
+ R for Data Science: <http://r4ds.had.co.nz/>


Introduction to tidyverse/R

Brandon LeBeau

January 10, 2018

Course Setup

Explore Data

First ggplot

Equivalent Code

Your Turn

Add Aesthetics

Global Aesthetics

Your Turn

Additional Geoms

Add more Aesthetics

Your Turn

Layered ggplot

Remove duplicate aesthetics

Your Turn

Brief plot customization

Brief plot customization Output

Additional ggplot2 resources

R works as a calculator

R Calculator 2

Can save objects to use later

R is case sensitive

R Functions

Working through Errors

Using dplyr for data manipulation

Data

Using filter

Save filtered results to object

Other operators for numbers

Your Turn

Filter character variables

Combine Operations - AND

Equivalent AND Statement

Filter - OR

%in%

Not Operator

Not Operator 2

Your Turn

All boolean options

Using count

Using arrange

Descending Order

Your Turn

Using select

Helper functions

starts_with helper

Contains helper

Colon

Drop variables

Reorder with everything

rename function

Your Turn

Using mutate

Your Turn

Using summarise

group_by

Explore trend

Explore trend output

Your Turn

group_by with mutate

group_by with mutate output

Chaining operations

The pipe %>% is the answer

The two are identical

Your Turn

Read in your own data

Data Import

Show Data

Other text formats

Your Turn

Excel Files

read_excel

Additional Resources

Using `dplyr` for data manipulation

Using `filter`

`%in%`

Using `count`

Using `arrange`

Using `select`

`starts_with` helper

Reorder with `everything`

`rename` function

Using `mutate`

Using `summarise`

`group_by`

`group_by` with `mutate`

`group_by` with `mutate` output

The pipe `%>%` is the answer

`read_excel`